Structured data

Structured data is information that has been organized into a predefined schema with a fixed format — most commonly rows and columns in a relational database table or a flat file such as a CSV. Each field has a defined data type, a consistent name, and a specific position in the record, making the data directly queryable with standard tools like SQL without any preprocessing. Examples include transaction records, product catalogs with attribute fields, customer account tables, inventory counts by location, and order management records. The defining characteristic is that the structure is explicit and machine-readable by design, not inferred.

Structured data is the historical foundation of enterprise analytics and remains the dominant data type in commerce operations — ERP systems, OMS platforms, and relational databases generate structured records for nearly every business transaction. From an AI perspective, structured data is relatively straightforward to work with: it can be fed directly into classical machine learning models, aggregated into features with well-understood transformations, and validated against schema expectations automatically. Its limitation is coverage: many of the richest signals about customer intent, product attributes, and operational context live in unstructured formats — text, images, voice — that structured databases cannot capture. Modern AI strategies in commerce must integrate both, using structured data as the reliable operational backbone and unstructured data as the source of richer contextual signal.